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The status of public resources in health has a positive and direct impact on this sector's outcomes because of its effect on the increase of social services. The government's ability to manage health expenditures greatly depends on identifying the determinants of these expenditures. Therefore, this study aimed to determine factors affecting the government health expenditures and estimate the related function in Iran during 2006-2011 using panel data. This was a cross-sectional and time-series study that was conducted using panel data analysis. In this study, the data were collected and categorized separately for each province from documents in the Ministry of Health and the Statistical Center of Iran. The results showed that there were positive associations between health expenditures and some factors including age group of 20 to 39 years (P =.04), the number of women (P =.001), the number of physicians, the number of hospital beds, and annual budget (P <.001). According to the results, it seems that allocating a part of health subsidies for increasing the insurance coverage of the age group of 20 to 39 years and also starting saving accounts can have an important effect on reducing health expenditures of this age group. © 2019 Wolters Kluwer Health, Inc. All rights reserved.

Artificial Intelligence (AI)-based systems are widely employed nowadays to make decisions that have far-reaching impact on individuals and society. Their decisions might affect everyone, everywhere, and anytime, entailing concerns about potential human rights issues. Therefore, it is necessary to move beyond traditional AI algorithms optimized for predictive performance and embed ethical and legal principles in their design, training, and deployment to ensure social good while still benefiting from the huge potential of the AI technology. The goal of this survey is to provide a broad multidisciplinary overview of the area of bias in AI systems, focusing on technical challenges and solutions as well as to suggest new research directions towards approaches well-grounded in a legal frame. In this survey, we focus on data-driven AI, as a large part of AI is powered nowadays by (big) data and powerful machine learning algorithms. If otherwise not specified, we use the general term bias to describe problems related to the gathering or processing of data that might result in prejudiced decisions on the bases of demographic features such as race, sex, and so forth. This article is categorized under: Commercial, Legal, and Ethical Issues > Fairness in Data Mining Commercial, Legal, and Ethical Issues > Ethical Considerations Commercial, Legal, and Ethical Issues > Legal Issues.